Overview

Brought to you by YData

Dataset statistics

Number of variables21
Number of observations1296675
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory188.0 MiB
Average record size in memory152.0 B

Variable types

Text7
Categorical4
Numeric9
DateTime1

Alerts

merch_long is highly overall correlated with zipHigh correlation
month is highly overall correlated with yearHigh correlation
unix_time is highly overall correlated with yearHigh correlation
year is highly overall correlated with month and 1 other fieldsHigh correlation
zip is highly overall correlated with merch_longHigh correlation
is_fraud is highly imbalanced (94.9%)Imbalance
amt is highly skewed (γ1 = 42.27787379)Skewed
day_of_week has 254282 (19.6%) zerosZeros

Reproduction

Analysis started2024-09-15 05:00:31.894384
Analysis finished2024-09-15 05:01:33.691255
Duration1 minute and 1.8 second
Software versionydata-profiling vv4.10.0
Download configurationconfig.json

Variables

Distinct693
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size9.9 MiB
2024-09-15T10:31:33.976267image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length43
Median length36
Mean length23.132597
Min length13

Characters and Unicode

Total characters29995460
Distinct characters55
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowfraud_Rippin, Kub and Mann
2nd rowfraud_Heller, Gutmann and Zieme
3rd rowfraud_Lind-Buckridge
4th rowfraud_Kutch, Hermiston and Farrell
5th rowfraud_Keeling-Crist
ValueCountFrequency (%)
and 474111
 
15.7%
llc 97780
 
3.2%
inc 91939
 
3.0%
sons 73145
 
2.4%
ltd 70853
 
2.3%
plc 66475
 
2.2%
group 50447
 
1.7%
fraud_kutch 10560
 
0.3%
fraud_schaefer 9394
 
0.3%
fraud_streich 9250
 
0.3%
Other values (804) 2069403
68.4%
2024-09-15T10:31:34.451622image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 2910697
 
9.7%
r 2695758
 
9.0%
d 2139780
 
7.1%
e 1865710
 
6.2%
u 1857912
 
6.2%
n 1768848
 
5.9%
1726682
 
5.8%
f 1397378
 
4.7%
_ 1296675
 
4.3%
o 1129340
 
3.8%
Other values (45) 11206680
37.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 22698472
75.7%
Uppercase Letter 3398527
 
11.3%
Space Separator 1726682
 
5.8%
Connector Punctuation 1296675
 
4.3%
Dash Punctuation 445070
 
1.5%
Other Punctuation 430034
 
1.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 2910697
12.8%
r 2695758
11.9%
d 2139780
9.4%
e 1865710
 
8.2%
u 1857912
 
8.2%
n 1768848
 
7.8%
f 1397378
 
6.2%
o 1129340
 
5.0%
i 1080395
 
4.8%
t 873637
 
3.8%
Other values (15) 4979017
21.9%
Uppercase Letter
ValueCountFrequency (%)
L 477174
14.0%
C 312176
 
9.2%
S 301639
 
8.9%
B 278515
 
8.2%
H 260640
 
7.7%
K 216627
 
6.4%
G 192442
 
5.7%
R 181447
 
5.3%
M 179139
 
5.3%
P 159738
 
4.7%
Other values (15) 838990
24.7%
Other Punctuation
ValueCountFrequency (%)
, 400966
93.2%
' 29068
 
6.8%
Space Separator
ValueCountFrequency (%)
1726682
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1296675
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 445070
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 26096999
87.0%
Common 3898461
 
13.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 2910697
 
11.2%
r 2695758
 
10.3%
d 2139780
 
8.2%
e 1865710
 
7.1%
u 1857912
 
7.1%
n 1768848
 
6.8%
f 1397378
 
5.4%
o 1129340
 
4.3%
i 1080395
 
4.1%
t 873637
 
3.3%
Other values (40) 8377544
32.1%
Common
ValueCountFrequency (%)
1726682
44.3%
_ 1296675
33.3%
- 445070
 
11.4%
, 400966
 
10.3%
' 29068
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 29995460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 2910697
 
9.7%
r 2695758
 
9.0%
d 2139780
 
7.1%
e 1865710
 
6.2%
u 1857912
 
6.2%
n 1768848
 
5.9%
1726682
 
5.8%
f 1397378
 
4.7%
_ 1296675
 
4.3%
o 1129340
 
3.8%
Other values (45) 11206680
37.4%

category
Categorical

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.9 MiB
gas_transport
131659 
grocery_pos
123638 
home
123115 
shopping_pos
116672 
kids_pets
113035 
Other values (9)
688556 

Length

Max length14
Median length12
Mean length10.526079
Min length4

Characters and Unicode

Total characters13648903
Distinct characters20
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmisc_net
2nd rowgrocery_pos
3rd rowentertainment
4th rowgas_transport
5th rowmisc_pos

Common Values

ValueCountFrequency (%)
gas_transport 131659
10.2%
grocery_pos 123638
9.5%
home 123115
9.5%
shopping_pos 116672
9.0%
kids_pets 113035
8.7%
shopping_net 97543
7.5%
entertainment 94014
7.3%
food_dining 91461
 
7.1%
personal_care 90758
 
7.0%
health_fitness 85879
 
6.6%
Other values (4) 228901
17.7%

Length

2024-09-15T10:31:34.639069image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
gas_transport 131659
10.2%
grocery_pos 123638
9.5%
home 123115
9.5%
shopping_pos 116672
9.0%
kids_pets 113035
8.7%
shopping_net 97543
7.5%
entertainment 94014
7.3%
food_dining 91461
 
7.1%
personal_care 90758
 
7.0%
health_fitness 85879
 
6.6%
Other values (4) 228901
17.7%

Most occurring characters

ValueCountFrequency (%)
s 1429026
10.5%
e 1287345
9.4%
o 1231724
9.0%
n 1193757
8.7%
p 1083847
 
7.9%
t 1076942
 
7.9%
_ 1039039
 
7.6%
r 917535
 
6.7%
i 833007
 
6.1%
a 665234
 
4.9%
Other values (10) 2891447
21.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 12609864
92.4%
Connector Punctuation 1039039
 
7.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 1429026
11.3%
e 1287345
10.2%
o 1231724
9.8%
n 1193757
9.5%
p 1083847
8.6%
t 1076942
8.5%
r 917535
7.3%
i 833007
 
6.6%
a 665234
 
5.3%
g 606425
 
4.8%
Other values (9) 2285022
18.1%
Connector Punctuation
ValueCountFrequency (%)
_ 1039039
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 12609864
92.4%
Common 1039039
 
7.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 1429026
11.3%
e 1287345
10.2%
o 1231724
9.8%
n 1193757
9.5%
p 1083847
8.6%
t 1076942
8.5%
r 917535
7.3%
i 833007
 
6.6%
a 665234
 
5.3%
g 606425
 
4.8%
Other values (9) 2285022
18.1%
Common
ValueCountFrequency (%)
_ 1039039
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13648903
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 1429026
10.5%
e 1287345
9.4%
o 1231724
9.0%
n 1193757
8.7%
p 1083847
 
7.9%
t 1076942
 
7.9%
_ 1039039
 
7.6%
r 917535
 
6.7%
i 833007
 
6.1%
a 665234
 
4.9%
Other values (10) 2891447
21.2%

amt
Real number (ℝ)

SKEWED 

Distinct52928
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.351035
Minimum1
Maximum28948.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.9 MiB
2024-09-15T10:31:34.829684image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.44
Q19.65
median47.52
Q383.14
95-th percentile196.31
Maximum28948.9
Range28947.9
Interquartile range (IQR)73.49

Descriptive statistics

Standard deviation160.31604
Coefficient of variation (CV)2.2788014
Kurtosis4545.645
Mean70.351035
Median Absolute Deviation (MAD)37.5
Skewness42.277874
Sum91222429
Variance25701.232
MonotonicityNot monotonic
2024-09-15T10:31:35.052889image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.14 542
 
< 0.1%
1.04 538
 
< 0.1%
1.25 535
 
< 0.1%
1.02 533
 
< 0.1%
1.01 523
 
< 0.1%
1.05 519
 
< 0.1%
1.2 516
 
< 0.1%
1.23 515
 
< 0.1%
1.08 512
 
< 0.1%
1.11 509
 
< 0.1%
Other values (52918) 1291433
99.6%
ValueCountFrequency (%)
1 222
< 0.1%
1.01 523
< 0.1%
1.02 533
< 0.1%
1.03 499
< 0.1%
1.04 538
< 0.1%
1.05 519
< 0.1%
1.06 471
< 0.1%
1.07 498
< 0.1%
1.08 512
< 0.1%
1.09 496
< 0.1%
ValueCountFrequency (%)
28948.9 1
< 0.1%
27390.12 1
< 0.1%
27119.77 1
< 0.1%
26544.12 1
< 0.1%
25086.94 1
< 0.1%
17897.24 1
< 0.1%
15305.95 1
< 0.1%
15047.03 1
< 0.1%
15034.18 1
< 0.1%
14849.74 1
< 0.1%

first
Text

Distinct352
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.9 MiB
2024-09-15T10:31:35.612821image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length11
Median length9
Mean length6.0804319
Min length3

Characters and Unicode

Total characters7884344
Distinct characters49
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJennifer
2nd rowStephanie
3rd rowEdward
4th rowJeremy
5th rowTyler
ValueCountFrequency (%)
christopher 26669
 
2.1%
robert 21667
 
1.7%
jessica 20581
 
1.6%
james 20039
 
1.5%
michael 20009
 
1.5%
david 19965
 
1.5%
jennifer 16940
 
1.3%
william 16371
 
1.3%
mary 16346
 
1.3%
john 16325
 
1.3%
Other values (342) 1101763
85.0%
2024-09-15T10:31:36.212193image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 1007700
 
12.8%
e 860878
 
10.9%
i 618247
 
7.8%
n 614453
 
7.8%
r 607072
 
7.7%
l 388220
 
4.9%
h 344993
 
4.4%
s 324237
 
4.1%
t 311569
 
4.0%
o 268849
 
3.4%
Other values (39) 2538126
32.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6587669
83.6%
Uppercase Letter 1296675
 
16.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 1007700
15.3%
e 860878
13.1%
i 618247
9.4%
n 614453
9.3%
r 607072
9.2%
l 388220
 
5.9%
h 344993
 
5.2%
s 324237
 
4.9%
t 311569
 
4.7%
o 268849
 
4.1%
Other values (16) 1241451
18.8%
Uppercase Letter
ValueCountFrequency (%)
J 218907
16.9%
M 144916
11.2%
S 114469
8.8%
A 112464
8.7%
C 106121
8.2%
D 86078
 
6.6%
K 85426
 
6.6%
R 70457
 
5.4%
T 66590
 
5.1%
L 62879
 
4.8%
Other values (13) 228368
17.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 7884344
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 1007700
 
12.8%
e 860878
 
10.9%
i 618247
 
7.8%
n 614453
 
7.8%
r 607072
 
7.7%
l 388220
 
4.9%
h 344993
 
4.4%
s 324237
 
4.1%
t 311569
 
4.0%
o 268849
 
3.4%
Other values (39) 2538126
32.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7884344
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 1007700
 
12.8%
e 860878
 
10.9%
i 618247
 
7.8%
n 614453
 
7.8%
r 607072
 
7.7%
l 388220
 
4.9%
h 344993
 
4.4%
s 324237
 
4.1%
t 311569
 
4.0%
o 268849
 
3.4%
Other values (39) 2538126
32.2%

last
Text

Distinct481
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.9 MiB
2024-09-15T10:31:36.559427image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length11
Median length10
Mean length6.1111774
Min length2

Characters and Unicode

Total characters7924211
Distinct characters48
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBanks
2nd rowGill
3rd rowSanchez
4th rowWhite
5th rowGarcia
ValueCountFrequency (%)
smith 28794
 
2.2%
williams 23605
 
1.8%
davis 21910
 
1.7%
johnson 20034
 
1.5%
rodriguez 17394
 
1.3%
martinez 14805
 
1.1%
jones 13976
 
1.1%
lewis 12753
 
1.0%
gonzalez 11799
 
0.9%
miller 11698
 
0.9%
Other values (471) 1119907
86.4%
2024-09-15T10:31:37.042083image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 786302
 
9.9%
r 658748
 
8.3%
a 648005
 
8.2%
n 609178
 
7.7%
o 583517
 
7.4%
l 489180
 
6.2%
s 487668
 
6.2%
i 435378
 
5.5%
t 288591
 
3.6%
h 228981
 
2.9%
Other values (38) 2708663
34.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6627536
83.6%
Uppercase Letter 1296675
 
16.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 786302
11.9%
r 658748
9.9%
a 648005
9.8%
n 609178
9.2%
o 583517
8.8%
l 489180
 
7.4%
s 487668
 
7.4%
i 435378
 
6.6%
t 288591
 
4.4%
h 228981
 
3.5%
Other values (15) 1411988
21.3%
Uppercase Letter
ValueCountFrequency (%)
M 158701
12.2%
W 106490
 
8.2%
S 105221
 
8.1%
C 93308
 
7.2%
B 84092
 
6.5%
R 83194
 
6.4%
H 81444
 
6.3%
G 75241
 
5.8%
J 71781
 
5.5%
P 66087
 
5.1%
Other values (13) 371116
28.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 7924211
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 786302
 
9.9%
r 658748
 
8.3%
a 648005
 
8.2%
n 609178
 
7.7%
o 583517
 
7.4%
l 489180
 
6.2%
s 487668
 
6.2%
i 435378
 
5.5%
t 288591
 
3.6%
h 228981
 
2.9%
Other values (38) 2708663
34.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7924211
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 786302
 
9.9%
r 658748
 
8.3%
a 648005
 
8.2%
n 609178
 
7.7%
o 583517
 
7.4%
l 489180
 
6.2%
s 487668
 
6.2%
i 435378
 
5.5%
t 288591
 
3.6%
h 228981
 
2.9%
Other values (38) 2708663
34.2%

gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.9 MiB
F
709863 
M
586812 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1296675
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowF
2nd rowF
3rd rowM
4th rowM
5th rowM

Common Values

ValueCountFrequency (%)
F 709863
54.7%
M 586812
45.3%

Length

2024-09-15T10:31:37.176579image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-15T10:31:37.293619image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
f 709863
54.7%
m 586812
45.3%

Most occurring characters

ValueCountFrequency (%)
F 709863
54.7%
M 586812
45.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1296675
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
F 709863
54.7%
M 586812
45.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 1296675
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
F 709863
54.7%
M 586812
45.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1296675
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
F 709863
54.7%
M 586812
45.3%

street
Text

Distinct983
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size9.9 MiB
2024-09-15T10:31:37.608173image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length35
Median length29
Mean length22.229027
Min length12

Characters and Unicode

Total characters28823823
Distinct characters62
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row561 Perry Cove
2nd row43039 Riley Greens Suite 393
3rd row594 White Dale Suite 530
4th row9443 Cynthia Court Apt. 038
5th row408 Bradley Rest
ValueCountFrequency (%)
apt 327791
 
6.4%
suite 305467
 
5.9%
island 22954
 
0.4%
michael 18967
 
0.4%
common 17978
 
0.3%
station 17957
 
0.3%
islands 17917
 
0.3%
david 17476
 
0.3%
brooks 16991
 
0.3%
fields 16321
 
0.3%
Other values (1940) 4376722
84.9%
2024-09-15T10:31:38.088988image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3859866
 
13.4%
e 1792676
 
6.2%
a 1454190
 
5.0%
i 1296969
 
4.5%
t 1248091
 
4.3%
r 1103208
 
3.8%
n 1066149
 
3.7%
s 1034564
 
3.6%
l 889594
 
3.1%
o 875571
 
3.0%
Other values (52) 14202945
49.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 14413030
50.0%
Decimal Number 6996528
24.3%
Space Separator 3859866
 
13.4%
Uppercase Letter 3226608
 
11.2%
Other Punctuation 327791
 
1.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1792676
12.4%
a 1454190
10.1%
i 1296969
9.0%
t 1248091
8.7%
r 1103208
 
7.7%
n 1066149
 
7.4%
s 1034564
 
7.2%
l 889594
 
6.2%
o 875571
 
6.1%
u 613916
 
4.3%
Other values (16) 3038102
21.1%
Uppercase Letter
ValueCountFrequency (%)
S 561924
17.4%
A 421707
13.1%
M 258180
 
8.0%
C 223839
 
6.9%
P 195864
 
6.1%
R 186303
 
5.8%
B 148676
 
4.6%
F 143149
 
4.4%
L 131665
 
4.1%
J 121164
 
3.8%
Other values (14) 834137
25.9%
Decimal Number
ValueCountFrequency (%)
5 748812
10.7%
3 739928
10.6%
2 734719
10.5%
7 703124
10.0%
1 693880
9.9%
8 692585
9.9%
6 677709
9.7%
0 677245
9.7%
4 669799
9.6%
9 658727
9.4%
Space Separator
ValueCountFrequency (%)
3859866
100.0%
Other Punctuation
ValueCountFrequency (%)
. 327791
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 17639638
61.2%
Common 11184185
38.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1792676
 
10.2%
a 1454190
 
8.2%
i 1296969
 
7.4%
t 1248091
 
7.1%
r 1103208
 
6.3%
n 1066149
 
6.0%
s 1034564
 
5.9%
l 889594
 
5.0%
o 875571
 
5.0%
u 613916
 
3.5%
Other values (40) 6264710
35.5%
Common
ValueCountFrequency (%)
3859866
34.5%
5 748812
 
6.7%
3 739928
 
6.6%
2 734719
 
6.6%
7 703124
 
6.3%
1 693880
 
6.2%
8 692585
 
6.2%
6 677709
 
6.1%
0 677245
 
6.1%
4 669799
 
6.0%
Other values (2) 986518
 
8.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28823823
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3859866
 
13.4%
e 1792676
 
6.2%
a 1454190
 
5.0%
i 1296969
 
4.5%
t 1248091
 
4.3%
r 1103208
 
3.8%
n 1066149
 
3.7%
s 1034564
 
3.6%
l 889594
 
3.1%
o 875571
 
3.0%
Other values (52) 14202945
49.3%

city
Text

Distinct894
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size9.9 MiB
2024-09-15T10:31:38.531872image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length25
Median length21
Mean length8.6522459
Min length3

Characters and Unicode

Total characters11219151
Distinct characters52
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMoravian Falls
2nd rowOrient
3rd rowMalad City
4th rowBoulder
5th rowDoe Hill
ValueCountFrequency (%)
city 21314
 
1.3%
west 19473
 
1.2%
north 14425
 
0.9%
saint 14363
 
0.9%
falls 12794
 
0.8%
new 11842
 
0.7%
mount 11375
 
0.7%
lake 11249
 
0.7%
san 10260
 
0.6%
springs 8727
 
0.5%
Other values (918) 1482445
91.6%
2024-09-15T10:31:39.083446image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 1090254
 
9.7%
a 935089
 
8.3%
n 821831
 
7.3%
o 817806
 
7.3%
l 781662
 
7.0%
r 748921
 
6.7%
i 704285
 
6.3%
t 598490
 
5.3%
s 446306
 
4.0%
321592
 
2.9%
Other values (42) 3952915
35.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 9277246
82.7%
Uppercase Letter 1619290
 
14.4%
Space Separator 321592
 
2.9%
Dash Punctuation 1023
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1090254
11.8%
a 935089
10.1%
n 821831
8.9%
o 817806
8.8%
l 781662
 
8.4%
r 748921
 
8.1%
i 704285
 
7.6%
t 598490
 
6.5%
s 446306
 
4.8%
d 309005
 
3.3%
Other values (15) 2023597
21.8%
Uppercase Letter
ValueCountFrequency (%)
C 156587
 
9.7%
M 147711
 
9.1%
S 136036
 
8.4%
B 133396
 
8.2%
H 115641
 
7.1%
W 95433
 
5.9%
P 92084
 
5.7%
L 86511
 
5.3%
R 79150
 
4.9%
A 74999
 
4.6%
Other values (15) 501742
31.0%
Space Separator
ValueCountFrequency (%)
321592
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1023
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 10896536
97.1%
Common 322615
 
2.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1090254
 
10.0%
a 935089
 
8.6%
n 821831
 
7.5%
o 817806
 
7.5%
l 781662
 
7.2%
r 748921
 
6.9%
i 704285
 
6.5%
t 598490
 
5.5%
s 446306
 
4.1%
d 309005
 
2.8%
Other values (40) 3642887
33.4%
Common
ValueCountFrequency (%)
321592
99.7%
- 1023
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11219151
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1090254
 
9.7%
a 935089
 
8.3%
n 821831
 
7.3%
o 817806
 
7.3%
l 781662
 
7.0%
r 748921
 
6.7%
i 704285
 
6.3%
t 598490
 
5.3%
s 446306
 
4.0%
321592
 
2.9%
Other values (42) 3952915
35.2%

state
Text

Distinct51
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.9 MiB
2024-09-15T10:31:39.336898image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2593350
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNC
2nd rowWA
3rd rowID
4th rowMT
5th rowVA
ValueCountFrequency (%)
tx 94876
 
7.3%
ny 83501
 
6.4%
pa 79847
 
6.2%
ca 56360
 
4.3%
oh 46480
 
3.6%
mi 46154
 
3.6%
il 43252
 
3.3%
fl 42671
 
3.3%
al 40989
 
3.2%
mo 38403
 
3.0%
Other values (41) 724142
55.8%
2024-09-15T10:31:39.684657image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 355776
13.7%
N 284464
 
11.0%
M 220694
 
8.5%
I 181993
 
7.0%
T 154353
 
6.0%
L 147877
 
5.7%
O 144031
 
5.6%
C 141011
 
5.4%
Y 131298
 
5.1%
X 94876
 
3.7%
Other values (14) 736977
28.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2593350
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 355776
13.7%
N 284464
 
11.0%
M 220694
 
8.5%
I 181993
 
7.0%
T 154353
 
6.0%
L 147877
 
5.7%
O 144031
 
5.6%
C 141011
 
5.4%
Y 131298
 
5.1%
X 94876
 
3.7%
Other values (14) 736977
28.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 2593350
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 355776
13.7%
N 284464
 
11.0%
M 220694
 
8.5%
I 181993
 
7.0%
T 154353
 
6.0%
L 147877
 
5.7%
O 144031
 
5.6%
C 141011
 
5.4%
Y 131298
 
5.1%
X 94876
 
3.7%
Other values (14) 736977
28.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2593350
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 355776
13.7%
N 284464
 
11.0%
M 220694
 
8.5%
I 181993
 
7.0%
T 154353
 
6.0%
L 147877
 
5.7%
O 144031
 
5.6%
C 141011
 
5.4%
Y 131298
 
5.1%
X 94876
 
3.7%
Other values (14) 736977
28.4%

zip
Real number (ℝ)

HIGH CORRELATION 

Distinct970
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48800.671
Minimum1257
Maximum99783
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.9 MiB
2024-09-15T10:31:39.831316image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1257
5-th percentile7208
Q126237
median48174
Q372042
95-th percentile94569
Maximum99783
Range98526
Interquartile range (IQR)45805

Descriptive statistics

Standard deviation26893.222
Coefficient of variation (CV)0.55108305
Kurtosis-1.0964493
Mean48800.671
Median Absolute Deviation (MAD)23068
Skewness0.079680758
Sum6.327861 × 1010
Variance7.2324542 × 108
MonotonicityNot monotonic
2024-09-15T10:31:39.983126image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
73754 3646
 
0.3%
34112 3613
 
0.3%
48088 3597
 
0.3%
82514 3527
 
0.3%
49628 3123
 
0.2%
15484 3123
 
0.2%
85173 3119
 
0.2%
29819 3117
 
0.2%
38761 3113
 
0.2%
5461 3112
 
0.2%
Other values (960) 1263585
97.4%
ValueCountFrequency (%)
1257 2023
0.2%
1330 1031
 
0.1%
1535 515
 
< 0.1%
1545 1024
 
0.1%
1612 519
 
< 0.1%
1843 2597
0.2%
1844 2058
0.2%
2180 519
 
< 0.1%
2630 2090
0.2%
2908 550
 
< 0.1%
ValueCountFrequency (%)
99783 1568
0.1%
99747 12
 
< 0.1%
99746 540
 
< 0.1%
99323 2572
0.2%
99160 3030
0.2%
99116 15
 
< 0.1%
99113 1047
 
0.1%
99033 2458
0.2%
98836 524
 
< 0.1%
98665 500
 
< 0.1%

city_pop
Real number (ℝ)

Distinct879
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean88824.441
Minimum23
Maximum2906700
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.9 MiB
2024-09-15T10:31:40.147742image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum23
5-th percentile139
Q1743
median2456
Q320328
95-th percentile525713
Maximum2906700
Range2906677
Interquartile range (IQR)19585

Descriptive statistics

Standard deviation301956.36
Coefficient of variation (CV)3.3994738
Kurtosis37.614519
Mean88824.441
Median Absolute Deviation (MAD)2198
Skewness5.5938531
Sum1.1517643 × 1011
Variance9.1177644 × 1010
MonotonicityNot monotonic
2024-09-15T10:31:40.296987image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
606 5496
 
0.4%
1595797 5130
 
0.4%
1312922 5075
 
0.4%
1766 4574
 
0.4%
241 4533
 
0.3%
2906700 4168
 
0.3%
276002 4155
 
0.3%
302 4147
 
0.3%
910148 4073
 
0.3%
198 4067
 
0.3%
Other values (869) 1251257
96.5%
ValueCountFrequency (%)
23 2049
0.2%
37 1013
 
0.1%
43 2034
0.2%
46 3040
0.2%
47 511
 
< 0.1%
49 1054
 
0.1%
51 1016
 
0.1%
52 518
 
< 0.1%
53 2610
0.2%
60 1045
 
0.1%
ValueCountFrequency (%)
2906700 4168
0.3%
2504700 2033
 
0.2%
2383912 521
 
< 0.1%
1595797 5130
0.4%
1577385 2563
0.2%
1526206 3517
0.3%
1417793 8
 
< 0.1%
1382480 2056
0.2%
1312922 5075
0.4%
1263321 3629
0.3%

job
Text

Distinct494
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.9 MiB
2024-09-15T10:31:40.591342image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length59
Median length38
Mean length20.227102
Min length3

Characters and Unicode

Total characters26227978
Distinct characters53
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPsychologist, counselling
2nd rowSpecial educational needs teacher
3rd rowNature conservation officer
4th rowPatent attorney
5th rowDance movement psychotherapist
ValueCountFrequency (%)
engineer 131756
 
4.6%
officer 110915
 
3.9%
manager 61124
 
2.1%
scientist 55878
 
1.9%
designer 52218
 
1.8%
surveyor 49062
 
1.7%
teacher 38126
 
1.3%
psychologist 32600
 
1.1%
research 29754
 
1.0%
editor 28725
 
1.0%
Other values (456) 2289024
79.5%
2024-09-15T10:31:41.077939image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 2803032
 
10.7%
i 2386346
 
9.1%
r 2198669
 
8.4%
a 1813638
 
6.9%
t 1782302
 
6.8%
n 1764769
 
6.7%
1582507
 
6.0%
o 1491775
 
5.7%
s 1444701
 
5.5%
c 1323152
 
5.0%
Other values (43) 7637087
29.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 22784440
86.9%
Space Separator 1582507
 
6.0%
Uppercase Letter 1369269
 
5.2%
Other Punctuation 443484
 
1.7%
Close Punctuation 24139
 
0.1%
Open Punctuation 24139
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 2803032
12.3%
i 2386346
10.5%
r 2198669
9.6%
a 1813638
 
8.0%
t 1782302
 
7.8%
n 1764769
 
7.7%
o 1491775
 
6.5%
s 1444701
 
6.3%
c 1323152
 
5.8%
l 999624
 
4.4%
Other values (16) 4776432
21.0%
Uppercase Letter
ValueCountFrequency (%)
C 156704
11.4%
E 145426
10.6%
P 143111
10.5%
S 137500
10.0%
T 113148
 
8.3%
M 89545
 
6.5%
A 88466
 
6.5%
F 68651
 
5.0%
D 58034
 
4.2%
R 55841
 
4.1%
Other values (11) 312843
22.8%
Other Punctuation
ValueCountFrequency (%)
, 312210
70.4%
/ 123567
 
27.9%
' 7707
 
1.7%
Space Separator
ValueCountFrequency (%)
1582507
100.0%
Close Punctuation
ValueCountFrequency (%)
) 24139
100.0%
Open Punctuation
ValueCountFrequency (%)
( 24139
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 24153709
92.1%
Common 2074269
 
7.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 2803032
11.6%
i 2386346
 
9.9%
r 2198669
 
9.1%
a 1813638
 
7.5%
t 1782302
 
7.4%
n 1764769
 
7.3%
o 1491775
 
6.2%
s 1444701
 
6.0%
c 1323152
 
5.5%
l 999624
 
4.1%
Other values (37) 6145701
25.4%
Common
ValueCountFrequency (%)
1582507
76.3%
, 312210
 
15.1%
/ 123567
 
6.0%
) 24139
 
1.2%
( 24139
 
1.2%
' 7707
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 26227978
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 2803032
 
10.7%
i 2386346
 
9.1%
r 2198669
 
8.4%
a 1813638
 
6.9%
t 1782302
 
6.8%
n 1764769
 
6.7%
1582507
 
6.0%
o 1491775
 
5.7%
s 1444701
 
5.5%
c 1323152
 
5.0%
Other values (43) 7637087
29.1%

dob
Date

Distinct968
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size9.9 MiB
Minimum1924-10-30 00:00:00
Maximum2005-01-29 00:00:00
2024-09-15T10:31:41.240805image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T10:31:41.397005image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

unix_time
Real number (ℝ)

HIGH CORRELATION 

Distinct1274823
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3492436 × 109
Minimum1.325376 × 109
Maximum1.3718168 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.9 MiB
2024-09-15T10:31:41.576118image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1.325376 × 109
5-th percentile1.328672 × 109
Q11.3387507 × 109
median1.3492497 × 109
Q31.3593854 × 109
95-th percentile1.3698306 × 109
Maximum1.3718168 × 109
Range46440799
Interquartile range (IQR)20634633

Descriptive statistics

Standard deviation12841278
Coefficient of variation (CV)0.0095173904
Kurtosis-1.0875405
Mean1.3492436 × 109
Median Absolute Deviation (MAD)10358807
Skewness0.0033779498
Sum1.7495305 × 1015
Variance1.6489843 × 1014
MonotonicityIncreasing
2024-09-15T10:31:41.802202image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1370177227 4
 
< 0.1%
1335110521 4
 
< 0.1%
1370050667 4
 
< 0.1%
1367602155 3
 
< 0.1%
1364686521 3
 
< 0.1%
1369587838 3
 
< 0.1%
1337306743 3
 
< 0.1%
1343668520 3
 
< 0.1%
1341944714 3
 
< 0.1%
1340650327 3
 
< 0.1%
Other values (1274813) 1296642
> 99.9%
ValueCountFrequency (%)
1325376018 1
< 0.1%
1325376044 1
< 0.1%
1325376051 1
< 0.1%
1325376076 1
< 0.1%
1325376186 1
< 0.1%
1325376248 1
< 0.1%
1325376282 1
< 0.1%
1325376308 1
< 0.1%
1325376318 1
< 0.1%
1325376361 1
< 0.1%
ValueCountFrequency (%)
1371816817 1
< 0.1%
1371816816 1
< 0.1%
1371816752 1
< 0.1%
1371816739 1
< 0.1%
1371816728 1
< 0.1%
1371816696 1
< 0.1%
1371816683 1
< 0.1%
1371816656 1
< 0.1%
1371816562 1
< 0.1%
1371816522 1
< 0.1%

merch_lat
Real number (ℝ)

Distinct1247805
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.537338
Minimum19.027785
Maximum67.510267
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.9 MiB
2024-09-15T10:31:42.024564image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum19.027785
5-th percentile29.751653
Q134.733572
median39.36568
Q341.957164
95-th percentile46.00353
Maximum67.510267
Range48.482482
Interquartile range (IQR)7.223592

Descriptive statistics

Standard deviation5.1097884
Coefficient of variation (CV)0.13259318
Kurtosis0.79599391
Mean38.537338
Median Absolute Deviation (MAD)3.397536
Skewness-0.18191543
Sum49970403
Variance26.109937
MonotonicityNot monotonic
2024-09-15T10:31:42.243073image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41.305966 4
 
< 0.1%
41.937796 4
 
< 0.1%
42.265012 4
 
< 0.1%
41.301611 4
 
< 0.1%
34.134994 4
 
< 0.1%
37.669788 4
 
< 0.1%
39.348185 4
 
< 0.1%
32.64469 4
 
< 0.1%
42.749184 4
 
< 0.1%
38.050673 4
 
< 0.1%
Other values (1247795) 1296635
> 99.9%
ValueCountFrequency (%)
19.027785 1
< 0.1%
19.027804 1
< 0.1%
19.029798 1
< 0.1%
19.031242 1
< 0.1%
19.032277 1
< 0.1%
19.033288 1
< 0.1%
19.034282 1
< 0.1%
19.034687 1
< 0.1%
19.035472 1
< 0.1%
19.036312 1
< 0.1%
ValueCountFrequency (%)
67.510267 1
< 0.1%
67.441518 1
< 0.1%
67.397018 1
< 0.1%
67.188111 1
< 0.1%
67.064277 1
< 0.1%
66.835174 1
< 0.1%
66.682905 1
< 0.1%
66.67355 1
< 0.1%
66.664673 1
< 0.1%
66.659242 1
< 0.1%

merch_long
Real number (ℝ)

HIGH CORRELATION 

Distinct1275745
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-90.226465
Minimum-166.67124
Maximum-66.950902
Zeros0
Zeros (%)0.0%
Negative1296675
Negative (%)100.0%
Memory size9.9 MiB
2024-09-15T10:31:42.443867image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-166.67124
5-th percentile-119.33009
Q1-96.897276
median-87.438392
Q3-80.236796
95-th percentile-73.354218
Maximum-66.950902
Range99.72034
Interquartile range (IQR)16.660479

Descriptive statistics

Standard deviation13.771091
Coefficient of variation (CV)-0.15262806
Kurtosis1.8484792
Mean-90.226465
Median Absolute Deviation (MAD)8.227889
Skewness-1.1469599
Sum-1.169944 × 108
Variance189.64294
MonotonicityNot monotonic
2024-09-15T10:31:42.638523image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-87.116414 4
 
< 0.1%
-81.219189 4
 
< 0.1%
-74.618269 4
 
< 0.1%
-85.326323 3
 
< 0.1%
-84.890305 3
 
< 0.1%
-88.49309 3
 
< 0.1%
-84.100102 3
 
< 0.1%
-97.527227 3
 
< 0.1%
-85.3444 3
 
< 0.1%
-86.037494 3
 
< 0.1%
Other values (1275735) 1296642
> 99.9%
ValueCountFrequency (%)
-166.671242 1
< 0.1%
-166.670132 1
< 0.1%
-166.669638 1
< 0.1%
-166.666179 1
< 0.1%
-166.664828 1
< 0.1%
-166.662888 1
< 0.1%
-166.661968 1
< 0.1%
-166.659277 1
< 0.1%
-166.657834 1
< 0.1%
-166.657174 1
< 0.1%
ValueCountFrequency (%)
-66.950902 1
< 0.1%
-66.955996 1
< 0.1%
-66.95654 1
< 0.1%
-66.958659 1
< 0.1%
-66.958751 1
< 0.1%
-66.959178 1
< 0.1%
-66.961923 1
< 0.1%
-66.962913 1
< 0.1%
-66.963918 1
< 0.1%
-66.963975 1
< 0.1%

is_fraud
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.9 MiB
0
1289169 
1
 
7506

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1296675
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1289169
99.4%
1 7506
 
0.6%

Length

2024-09-15T10:31:42.806646image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-15T10:31:42.945899image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 1289169
99.4%
1 7506
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 1289169
99.4%
1 7506
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1296675
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1289169
99.4%
1 7506
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common 1296675
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1289169
99.4%
1 7506
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1296675
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1289169
99.4%
1 7506
 
0.6%

year
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.9 MiB
2019
924850 
2020
371825 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters5186700
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019
2nd row2019
3rd row2019
4th row2019
5th row2019

Common Values

ValueCountFrequency (%)
2019 924850
71.3%
2020 371825
28.7%

Length

2024-09-15T10:31:43.427225image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-15T10:31:43.526477image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
2019 924850
71.3%
2020 371825
28.7%

Most occurring characters

ValueCountFrequency (%)
2 1668500
32.2%
0 1668500
32.2%
1 924850
17.8%
9 924850
17.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5186700
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 1668500
32.2%
0 1668500
32.2%
1 924850
17.8%
9 924850
17.8%

Most occurring scripts

ValueCountFrequency (%)
Common 5186700
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 1668500
32.2%
0 1668500
32.2%
1 924850
17.8%
9 924850
17.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5186700
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 1668500
32.2%
0 1668500
32.2%
1 924850
17.8%
9 924850
17.8%

month
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.1421497
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.9 MiB
2024-09-15T10:31:43.634094image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4177033
Coefficient of variation (CV)0.55643439
Kurtosis-1.0475463
Mean6.1421497
Median Absolute Deviation (MAD)3
Skewness0.29851575
Sum7964372
Variance11.680696
MonotonicityNot monotonic
2024-09-15T10:31:43.785326image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
5 146875
11.3%
6 143811
11.1%
3 143789
11.1%
12 141060
10.9%
4 134970
10.4%
1 104727
8.1%
2 97657
7.5%
8 87359
6.7%
7 86596
6.7%
9 70652
5.4%
Other values (2) 139179
10.7%
ValueCountFrequency (%)
1 104727
8.1%
2 97657
7.5%
3 143789
11.1%
4 134970
10.4%
5 146875
11.3%
6 143811
11.1%
7 86596
6.7%
8 87359
6.7%
9 70652
5.4%
10 68758
5.3%
ValueCountFrequency (%)
12 141060
10.9%
11 70421
5.4%
10 68758
5.3%
9 70652
5.4%
8 87359
6.7%
7 86596
6.7%
6 143811
11.1%
5 146875
11.3%
4 134970
10.4%
3 143789
11.1%

day
Real number (ℝ)

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.587978
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.9 MiB
2024-09-15T10:31:43.916313image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median15
Q323
95-th percentile30
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.8291214
Coefficient of variation (CV)0.5664058
Kurtosis-1.1871417
Mean15.587978
Median Absolute Deviation (MAD)8
Skewness0.030847364
Sum20212542
Variance77.953384
MonotonicityNot monotonic
2024-09-15T10:31:44.039860image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1 47089
 
3.6%
15 46213
 
3.6%
8 46201
 
3.6%
16 44894
 
3.5%
2 44748
 
3.5%
9 44685
 
3.4%
7 44239
 
3.4%
14 44015
 
3.4%
28 43470
 
3.4%
17 42272
 
3.3%
Other values (21) 848849
65.5%
ValueCountFrequency (%)
1 47089
3.6%
2 44748
3.5%
3 41842
3.2%
4 41479
3.2%
5 41886
3.2%
6 41420
3.2%
7 44239
3.4%
8 46201
3.6%
9 44685
3.4%
10 41934
3.2%
ValueCountFrequency (%)
31 24701
1.9%
30 41019
3.2%
29 39617
3.1%
28 43470
3.4%
27 39684
3.1%
26 40692
3.1%
25 40374
3.1%
24 41360
3.2%
23 40815
3.1%
22 42061
3.2%

day_of_week
Real number (ℝ)

ZEROS 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0706037
Minimum0
Maximum6
Zeros254282
Zeros (%)19.6%
Negative0
Negative (%)0.0%
Memory size4.9 MiB
2024-09-15T10:31:44.147978image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.1981526
Coefficient of variation (CV)0.71586984
Kurtosis-1.445049
Mean3.0706037
Median Absolute Deviation (MAD)2
Skewness-0.078453041
Sum3981575
Variance4.8318747
MonotonicityNot monotonic
2024-09-15T10:31:44.249666image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 254282
19.6%
6 250579
19.3%
5 200957
15.5%
1 160227
12.4%
4 152272
11.7%
3 147285
11.4%
2 131073
10.1%
ValueCountFrequency (%)
0 254282
19.6%
1 160227
12.4%
2 131073
10.1%
3 147285
11.4%
4 152272
11.7%
5 200957
15.5%
6 250579
19.3%
ValueCountFrequency (%)
6 250579
19.3%
5 200957
15.5%
4 152272
11.7%
3 147285
11.4%
2 131073
10.1%
1 160227
12.4%
0 254282
19.6%

Interactions

2024-09-15T10:31:26.462708image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T10:31:10.349433image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T10:31:12.359777image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T10:31:14.388536image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T10:31:16.372676image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T10:31:18.296929image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T10:31:20.351652image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T10:31:22.337613image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T10:31:24.519956image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T10:31:26.755908image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T10:31:10.569703image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T10:31:12.577457image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T10:31:14.606774image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T10:31:16.585893image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T10:31:18.554097image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T10:31:20.570977image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T10:31:22.551087image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T10:31:24.732754image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T10:31:26.997560image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T10:31:10.783479image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T10:31:12.789372image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T10:31:14.823940image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T10:31:16.796868image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T10:31:18.811143image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T10:31:20.788710image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T10:31:22.759971image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T10:31:24.963928image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T10:31:27.211987image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T10:31:11.030112image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T10:31:13.043329image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T10:31:15.067768image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T10:31:17.032414image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T10:31:19.057546image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T10:31:21.041711image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T10:31:23.003236image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T10:31:25.179296image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T10:31:27.419555image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T10:31:11.242791image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T10:31:13.262574image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T10:31:15.281856image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T10:31:17.242934image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T10:31:19.268284image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T10:31:21.271198image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T10:31:23.209274image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T10:31:25.389124image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T10:31:27.624300image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T10:31:11.446790image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T10:31:13.485620image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T10:31:15.492138image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T10:31:17.449585image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T10:31:19.477396image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T10:31:21.478105image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T10:31:23.416343image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T10:31:25.592767image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T10:31:27.831807image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T10:31:11.656309image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T10:31:13.700854image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T10:31:15.704817image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T10:31:17.652391image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T10:31:19.688545image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T10:31:21.686470image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T10:31:23.614805image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T10:31:25.800431image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T10:31:28.061642image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T10:31:11.863260image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T10:31:13.947287image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T10:31:15.922385image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T10:31:17.858282image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T10:31:19.903607image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T10:31:21.902743image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T10:31:24.102049image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T10:31:26.027802image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T10:31:28.265537image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T10:31:12.136856image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T10:31:14.172367image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T10:31:16.158794image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T10:31:18.079170image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T10:31:20.133874image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T10:31:22.129165image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T10:31:24.311765image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-15T10:31:26.251097image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2024-09-15T10:31:44.339413image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
amtcategorycity_popdayday_of_weekgenderis_fraudmerch_latmerch_longmonthunix_timeyearzip
amt1.0000.020-0.0240.000-0.0010.0000.0000.0120.000-0.0030.0010.0000.001
category0.0201.0000.0140.0010.0030.0540.0710.0110.0090.0010.0010.0000.011
city_pop-0.0240.0141.000-0.0010.0020.0890.004-0.2640.0860.001-0.0030.001-0.040
day0.0000.001-0.0011.0000.0170.0000.009-0.0000.0000.0080.0190.057-0.001
day_of_week-0.0010.0030.0020.0171.0000.0060.0120.0000.0010.038-0.0290.090-0.001
gender0.0000.0540.0890.0000.0061.0000.0080.1030.0820.0020.0000.0000.119
is_fraud0.0000.0710.0040.0090.0120.0081.0000.0080.0050.0180.0180.0030.005
merch_lat0.0120.011-0.264-0.0000.0000.1030.0081.0000.104-0.0010.0010.000-0.162
merch_long0.0000.0090.0860.0000.0010.0820.0050.1041.000-0.001-0.0010.000-0.957
month-0.0030.0010.0010.0080.0380.0020.018-0.001-0.0011.0000.1730.5270.001
unix_time0.0010.001-0.0030.019-0.0290.0000.0180.001-0.0010.1731.0000.9710.001
year0.0000.0000.0010.0570.0900.0000.0030.0000.0000.5270.9711.0000.002
zip0.0010.011-0.040-0.001-0.0010.1190.005-0.162-0.9570.0010.0010.0021.000

Missing values

2024-09-15T10:31:28.773997image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-09-15T10:31:30.230080image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

merchantcategoryamtfirstlastgenderstreetcitystatezipcity_popjobdobunix_timemerch_latmerch_longis_fraudyearmonthdayday_of_week
0fraud_Rippin, Kub and Mannmisc_net4.97JenniferBanksF561 Perry CoveMoravian FallsNC286543495Psychologist, counselling1988-03-09132537601836.011293-82.04831502019111
1fraud_Heller, Gutmann and Ziemegrocery_pos107.23StephanieGillF43039 Riley Greens Suite 393OrientWA99160149Special educational needs teacher1978-06-21132537604449.159047-118.18646202019111
2fraud_Lind-Buckridgeentertainment220.11EdwardSanchezM594 White Dale Suite 530Malad CityID832524154Nature conservation officer1962-01-19132537605143.150704-112.15448102019111
3fraud_Kutch, Hermiston and Farrellgas_transport45.00JeremyWhiteM9443 Cynthia Court Apt. 038BoulderMT596321939Patent attorney1967-01-12132537607647.034331-112.56107102019111
4fraud_Keeling-Cristmisc_pos41.96TylerGarciaM408 Bradley RestDoe HillVA2443399Dance movement psychotherapist1986-03-28132537618638.674999-78.63245902019111
5fraud_Stroman, Hudson and Erdmangas_transport94.63JenniferConnerF4655 David IslandDublinPA189172158Transport planner1961-06-19132537624840.653382-76.15266702019111
6fraud_Rowe-Vandervortgrocery_net44.54KelseyRichardsF889 Sarah Station Suite 624HolcombKS678512691Arboriculturist1993-08-16132537628237.162705-100.15337002019111
7fraud_Corwin-Collinsgas_transport71.65StevenWilliamsM231 Flores Pass Suite 720EdinburgVA228246018Designer, multimedia1947-08-21132537630838.948089-78.54029602019111
8fraud_Herzog Ltdmisc_pos4.27HeatherChaseF6888 Hicks Stream Suite 954ManorPA156651472Public affairs consultant1941-03-07132537631840.351813-79.95814602019111
9fraud_Schoen, Kuphal and Nitzschegrocery_pos198.39MelissaAguilarF21326 Taylor Squares Suite 708ClarksvilleTN37040151785Pathologist1974-03-28132537636137.179198-87.48538102019111
merchantcategoryamtfirstlastgenderstreetcitystatezipcity_popjobdobunix_timemerch_latmerch_longis_fraudyearmonthdayday_of_week
1296665fraud_Gulgowski LLChome72.17JamesHuntM7369 Gabriel TunnelPointe Aux PinsMI4977595Electrical engineer1994-02-09137181652244.938461-83.996234020206216
1296666fraud_Hyatt, Russel and Gleichnerhealth_fitness7.30AmberLewisF6296 John Keys Suite 858Pembroke TownshipIL609582135Psychotherapist, child2004-05-08137181656240.556811-88.092339020206216
1296667fraud_Hahn, Douglas and Schowaltertravel19.71ChristopherFarrellM97070 Anderson LandHaines CityFL3384433804Exercise physiologist1991-01-01137181665627.465871-81.511804020206216
1296668fraud_Metz, Russel and Metzkids_pets100.85MargaretCurtisF742 Oneill ShoreFlorenceMS3907319685Fine artist1984-12-24137181668331.377697-90.528450020206216
1296669fraud_Stiedemann Incmisc_pos37.38MarissaPowellF474 Allen HavenNorth LoupNE68859509Nurse, children's1980-09-15137181669641.728638-99.039660020206216
1296670fraud_Reichel Incentertainment15.56ErikPattersonM162 Jessica Row Apt. 072HatchUT84735258Geoscientist1961-11-24137181672836.841266-111.690765020206216
1296671fraud_Abernathy and Sonsfood_dining51.70JeffreyWhiteM8617 Holmes Terrace Suite 651TuscaroraMD21790100Production assistant, television1979-12-11137181673938.906881-78.246528020206216
1296672fraud_Stiedemann Ltdfood_dining105.93ChristopherCastanedaM1632 Cohen Drive Suite 639High Rolls Mountain ParkNM88325899Naval architect1967-08-30137181675233.619513-105.130529020206216
1296673fraud_Reinger, Weissnat and Strosinfood_dining74.90JosephMurrayM42933 Ryan UnderpassMandersonSD577561126Volunteer coordinator1980-08-18137181681642.788940-103.241160020206216
1296674fraud_Langosh, Wintheiser and Hyattfood_dining4.30JeffreySmithM135 Joseph MountainsSulaMT59871218Therapist, horticultural1995-08-16137181681746.565983-114.186110020206216